The unleashed microbiome of cancer patients as a discovery platform for rational microbiome engineering

癌症患者释放的微生物组作为合理微生物组工程的发现平台

基本信息

批准号：
10671473
负责人：
Jonas Schluter
金额：
$ 50.85万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-13 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10671473
关键词：
16S ribosomal RNA sequencing Affect Algorithms Artificial Intelligence Bayesian Analysis Biological Blood Blood Chemical Analysis Blood specimen Cancer Patient Categories Cells Cohort Studies Complex Data Data Set Data Sources Decision Making Diet Diet Records Disease Drug Kinetics Drug Targeting Ecology Ecosystem Engineering Etiology Event Exclusion Food Frequencies Future Gut Mucosa Health Hematopoietic Stem Cell Transplantation Human Human body Immune Immune system Immunomodulators Immunophenotyping In Situ In Vitro Infection Leukocytes Machine Learning Measurement Medical Records Metadata Metagenomics Methods Microbe Nutritional Online Systems Patients Pharmaceutical Preparations Physiological Physiology Publishing Records Research Resolution Resources Ruminococcus Sampling Scanning Shotguns Stream Structure Tacrolimus Taxonomy Techniques Testing Therapeutic Time Trees Visualization commensal microbes complex data cost data visualization experience experimental study gut microbiome gut microbiota high dimensionality immunoregulation in silico large datasets microbial microbiome multidimensional data negative affect next generation novel pathogen phenotypic data stool sample structured data success therapeutic target tool unsupervised learning web based interface

项目摘要

Project Summary The human gut microbiome is associated with a range of diseases, and may positively or negatively affect the success of therapies. However, the causal directions between the human gut microbiome and host health are seldomly clear due to a lack of feasible controlled experiments in humans. High resolution, high frequency temporal data of paired microbiome and physiological measurement, and rich metadata of potential confounders, allow the application of causal inference frameworks. Such data can be mined for potential microbiome drivers of host health, especially if both the microbiome and host physiology are perturbed during the time courses. We have recently published a vast longitudinal microbiome set from cancer patients undergoing severe perturbation of their immune system. Concurrently, these patients experience dramatic shifts in their gut ecosystem. We here propose to unlock this data and build a discovery platform for microbiome causality, towards rational microbiome engineering. We will develop a new machine-learning technique that enables rapid exploration of our data through effective visualization and a web-based interface. We will develop a new method to identify gut microbial competitor species of common pathogens, which are systematically missed by existing approaches but are representing the most promising targets for microbiome engineering. Finally, we will elucidate the bidirectional interplay between human- targeted medications and the gut microbiome. For this, we will leverage our large data set of paired microbiome and host immune cell trajectories. This will validate recent in vitro results indicating that human-targeted medications may influence gut ecology using in situ data, and it will identify potential gut microbiome modulation of pharmacokinetics.

项目概要人类肠道微生物组与一系列疾病相关，并且可能对疾病产生积极影响或对治疗的成功产生负面影响。然而，两者之间的因果方向由于缺乏可行的方法，人类肠道微生物组和宿主健康状况很少清楚人体对照实验。高分辨率、高频时间数据配对的微生物组和生理测量，以及丰富的潜在元数据混杂因素，允许应用因果推理框架。这样的数据可以是挖掘宿主健康的潜在微生物组驱动因素，特别是如果微生物组和宿主的生理机能在时间过程中受到干扰。我们最近发布了来自经历严重扰动的癌症患者的大量纵向微生物组他们的免疫系统。与此同时，这些患者的生活状况也发生了巨大的变化。肠道生态系统。我们在这里建议解锁这些数据并建立一个发现平台微生物组因果关系，走向合理的微生物组工程。我们将开发一个新的机器学习技术可以通过有效的方式快速探索我们的数据可视化和基于网络的界面。我们将开发一种新方法来识别肠道常见病原体的微生物竞争物种，这些物种被系统地错过现有方法，但代表了微生物组最有希望的目标工程。最后，我们将阐明人类之间的双向相互作用靶向药物和肠道微生物群。为此，我们将利用我们的大数据一组配对的微生物组和宿主免疫细胞轨迹。这将验证最近的体外结果表明人类靶向药物可能影响肠道生态使用原位数据，它将识别潜在的肠道微生物组调节药代动力学。